I am training a classification random forest for object detection in images. I have several features (like HoG, edge features etc) which work good enough separately. But when I train using all features together, the results don't improve. E.g. area under curve are as follows:
HoG Features: 0.90
edge Features: 0.81
Combined together: 0.86
I am using scikit-learn random forest library, # of trees = 200, information gain = 'entropy', 2 classes and I have 4000 training examples.